Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2019

Abstract

Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness compared to previous work via a user-study and its potential applications via a case study using real-world logs.

Keywords

Log analysis, Model inference, software testing

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ASE '19: Proceedings of the 34th ACM/IEEE International Conference on Automated Software Engineering, San Diego, November 11-15

First Page

851

Last Page

862

ISBN

9781728125084

Identifier

10.1109/ASE.2019.00084

Publisher

ACM

City or Country

New York

Embargo Period

4-2-2020

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ASE.2019.00084

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